Remote Data & AI Engineer — Lakehouse & MLOps

RevStar; Williamstown (NJ); Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Remote-First

Health Coverage

401(k) Retirement Plan

Mentorship & Coaching

Professional Growth Opportunities

Company Outings & Volunteer

Job summary

RevStar is seeking a Data & AI Engineer to design greenfield Lakehouse solutions and production-grade AI models. You will work with data architects, scientists, and client leaders to turn complex data into scalable AI solutions.

Responsibilities include building ETL/ELT pipelines across multi-cloud environments, optimizing Spark workloads, and integrating MLOps with MLflow. You will contribute to data governance and production deployments, with emphasis on secure, scalable architectures.

Qualifications

• 3+ years in cloud-native data engineering with Databricks/Spark/Delta Lake.
• 2+ years hands-on Databricks, Spark, Delta Lake, MLflow experience.
• Proficiency in Python, SQL, Spark, CI/CD, and Infrastructure-as-Code (Terraform or Databricks CLI).
• Active Databricks certification is highly preferred.

Responsibilities

• Design, build, and optimize scalable ETL/ELT pipelines across multi-cloud environments.
• Operationalize AI models inside Databricks using MLflow and maintain CI/CD pipelines.
• Enforce data governance and collaborate with clients to translate AI data needs into outcomes.
• Document solutions and lead handoffs to client engineering teams.

Skills

Python

SQL

Apache Spark

Delta Lake

MLflow

CI/CD

Terraform

Databricks CLI

Tools

Databricks

MLflow

Job description

RevStar is seeking a Data & AI Engineer to design greenfield Lakehouse solutions and production-grade AI models. You will work with data architects, scientists, and client leaders to turn complex data into scalable AI solutions.

Responsibilities include building ETL/ELT pipelines across multi-cloud environments, optimizing Spark workloads, and integrating MLOps with MLflow. You will contribute to data governance and production deployments, with emphasis on secure, scalable architectures.